The present exemplary embodiment relates to the discovery of new chemicals that can be used for the treatment, prevention, or amelioration of hyperproliferative diseases and/or disorders, such as cancer. It finds particular application in conjunction with a computer based methodology for identifying new chemical compositions, and will be described with particular reference thereto. However, it is to be appreciated that the present exemplary embodiment is also amenable to other like applications.
Cancer is a class of diseases in which groups of cells display uncontrolled growth, intrusion and destruction of adjacent tissues, and sometimes spread to other locations in the body. Cancer treatments are generally designed to slow the progress of the cancer or eliminate it entirely. Various treatments include radiation therapy, immunotherapy, hormonal therapy, gene therapy, chemotherapy, targeted therapy and surgical procedures. All of these approaches pose significant drawbacks for the patient. Surgery, for example, may be contraindicated due to the health of the patient or may be unacceptable to the patient. Additionally, surgery may not completely remove the neoplastic tissue. Radiation therapy is only effective when the neoplastic tissue exhibits a higher sensitivity to radiation than normal tissue, and radiation therapy can also often elicit serious side effects. Hormonal therapy is rarely given as a single agent and although can be effective, is often used to prevent or delay recurrence of cancer after other treatments have removed the majority of the cancer cells. Biological therapies/immunotherapies are limited in number and may produce side effects such as rashes or swellings, flu-like symptoms, including fever, chills and fatigue, digestive tract problems or allergic reactions.
Many potential drugs have been discovered in the last 30 years for treating cancer. In fact, a large number of different cancers are treated successfully and produce strong remissions that often prevent the cancers of regaining strength. The mechanisms by which these results are obtained are to kill the cells by interfering with the reproductive machinery of cell replication. For example, standard cancer chemotherapeutic drugs kill cancer cells upon induction of programmed cell death (“apoptosis”) by targeting basic cellular processes and molecules. These basic cellular processes and molecules include RNA/DNA (alkylating and carbamylating agents, platin analogs and topoisomerase inhibitors), metabolism (drugs of this class are named anti-metabolites and examples are folic acid, purin and pyrimidine antagonist) as well as the mitotic spindle apparatus with αβ-tubulin heterodimers as the essential component (drugs are categorized into stabilizing and destabilizing tubulin inhibitors; examples are Taxol/Paclitaxel®, Docetaxel/Taxotere® and vinca alkaloids). A significant majority of cancer chemotherapeutics act by inhibiting DNA synthesis, either directly, or indirectly by inhibiting the biosynthesis of the deoxyribonucleotide triphosphate precursors, to prevent DNA replication and concomitant cell division. These agents, which include alkylating agents, such as nitrosourea, anti-metabolites, such as methotrexate and hydroxyurea, and other agents, such as etoposides, campathecins, bleomycin, doxorubicin, daunorubicin, etc., although not necessarily cell cycle specific, kill cells during S phase because of their effect on DNA replication. Other agents, specifically colchicine and the vinca alkaloids, such as vinblastine and vincristine, interfere with microtubule assembly resulting in mitotic arrest.
Despite the availability of a variety of chemotherapeutic agents, chemotherapy has many drawbacks. Almost all chemotherapeutic agents are toxic, and chemotherapy causes significant, and often dangerous, side effects, including severe nausea, bone marrow depression, immunosuppression, etc. Additionally, even with administration of combinations of chemotherapeutic agents, many tumor cells are resistant or develop resistance to the chemotherapeutic agents. In fact, those cells resistant to the particular chemotherapeutic agents used in the treatment protocol often prove to be resistant to other drugs, even those agents that act by mechanisms different from the mechanisms of action of the drugs used in the specific treatment; this phenomenon is termed pleiotropic drug or multidrug resistance. Thus, because of drug resistance, many cancers prove refractory to standard chemotherapeutic treatment protocols.
Because some of these drugs are carefully designed to interfere with the replication of fast growing cells, they also often interfere with the replication of those non-carcinogenic cells that also constantly replicate, such as hair, gut lining and so on. As a result, these drugs have to be used at low doses in order to minimize the terrifying effects of the treatments. The challenge is therefore how to create potent and specific cancer cells killing agents, or inhibiting agents with minimal side effects, and, notably without killing other reproducing cells.
The Mcase program was originally developed and is presently widely used by regulatory agencies and pharmaceutical research companies to replace laboratory animals in the evaluation of the potential toxic effect of chemicals. The program is based on hierarchical statistical analysis of a database (a training set) composed of a number of chemicals with their biological activity data. The program aims to discover substructures that appear mostly in active molecules and may therefore be responsible for the observed activity. The Mcase program begins by identifying the most statistically significant substructure existing within the learning set. This fragment is labeled a biophore, and is responsible for the activity of the largest possible number of active molecules. The active molecules containing this biophore are then removed from the database, and the remaining ones are submitted to a new analysis leading to the identification of the next biophore. This procedure is repeated until either the activity of all the molecules in the learning set have been accounted for or no additional statistically significant substructure can be found. For each set of molecules containing a specific biophore, Mcase identifies additional parameters, deemed modulators, which can be used in the construction of a quantitative structure-activity relationship within this reduced set of congeneric molecules. Modulators consist of the presence of certain substructures or the value of calculated parameters, such as the highest occupied and lowest unoccupied orbital energies, octanol-water partition coefficient and so on. The process is automated and proceeds with minimal human intervention and bias. The knowledge that the program gains during the training process can then be used to predict the biological activity of new chemicals that were not included in the training set.
Advantageously, the present disclosure provides a method for identifying new chemical compositions that are active for treating, preventing or ameliorating hyperproliferative disease and/or disorders such as cancer with minimal or none of the side effects often associated with chemotherapy. By not killing normally reproducing cells, drugs which do not exhibit high potency can be used in large doses that might still be sufficient to realize the medical objective. However, while the concept is simple, the realization is elusive and has not been achieved so far in a rational and meaningful way.